Assessing the effects of forest management techniques on sequestering carbon in northern woodlots
Bibliographic record
Abstract
Canadian woodlots can play an important role in mitigating climate change through increased carbon sequestration. I conducted a survey of private woodlot owners in Ontario to address three questions related to forest carbon storage and forest management techniques (FMT). The survey responses showed that the largest portion of woodlot owners in this study (46%) is not actively engaged in forest management on their properties, opting for natural succession. Using the data from the survey, I completed four sets of simulations with the CBM-CFS3 model. The simulation results indicated that current carbon storage on the woodlots is 240,753 tons and, if all the landowners let their forests grow without management (natural succession), in 300 years, carbon storage will increase to 501,236 tons. The FMT that stored the greatest amount of carbon over the long-term was a 10% commercial thinning (665,007 tons). Adding a 60-year rotation interval to the 10% commercial thinning increased carbon storage even more (791,027 tons). Conversely, clearcuts and wildfires had devastating effects on carbon storage. After a clearcut or wildfire, transitioning to a red pine forest recovered more lost carbon than any FMT or natural succession. All of these are long-term perspectives, but in the short-term, natural succession may be the best method for storing carbon. However, what made this investigation most interesting was the complexities of the woodlots themselves, their stand make-up, ownership and uses. The diversity of these woodlots may offer a path of least resistance to increasing carbon storage on them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".